Inline quality rating of multi‐crystalline wafers based on photoluminescence images. (11th November 2015)

• Record Type: Journal Article
• Authors: Demant, Matthias; Rein, Stefan; Haunschild, Jonas; Strauch, Theresa; Höffler, Hannes; Broisch, Juliane; Wasmer, Sven; Sunder, Kirsten; Anspach, Oliver; Brox, Thomas
• Is Part Of: Progress in photovoltaics. Volume 24:Number 12(2016)
• Page Start: 1533
• Page End: 1546
• Publication Date: 2015-11-11
• Subjects: multi‐crystalline silicon -- photoluminescence -- wafer inspection -- quality control -- pattern recognition -- elastic‐net; Solar cells -- Periodicals; Photovoltaic cells -- Periodicals; Solar power plants -- Periodicals; 621.31245
• DOI: 10.1002/pip.2706
• Languages: English
• ISSNs: 1062-7995

Abstract

Abstract: The quality assessment of multi‐crystalline and high‐performance multi‐crystalline silicon wafers during incoming inspection of solar cell production requires a reproducible description of the relevant material defects and a classification scheme that is capable to rate as‐cut wafers from unknown manufacturers. Both needs are addressed in this work. We introduce an image processing framework that allows the various types of crystallization‐related defects visible in photoluminescence images to be detected quantitatively and thus enables a complete wafer description in terms of defects. The importance of different features within this defect characteristic is weighted by predicting the open‐circuit voltage of solar cells with aluminum back‐surface field as well as passivated emitter and rear cells with a stepwise extension of the model. The resulting robust classification scheme is successfully evaluated on a set of 7500 wafers, which represents the whole spectrum of material types and qualities that are currently available at the market. A comparison of defect signatures in high‐performance multi‐crystalline and standard multi‐crystalline silicon materials underlines the relevance of additional features. As a result of this paper, we show that a regularized version of multi‐linear regression models for quality prediction can optimize simpler linear models by adding selected features to the defect characteristic leading to mean absolute prediction errors of 2.2 mV for solar cells with aluminum back‐surface field and 2.9 mV for passivated emitter and rear cells solar cells in a true blind test. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : The paper shows how to rate the quality of as‐cut multi‐crystalline silicon (mc‐Si) wafers. Advanced pattern recognition techniques are successfully applied to systematically analyze a broad spectrum of wafer materials: We quantify defect structures in photoluminescence images of standard mc‐Si and high‐performance mc‐Si wafers sampled from over 72 bricks. We develop rating schemes to predict the open‐circuit voltage of solar cells with aluminum back‐surface field and passivated emitter and rear cells with mean absolute error of 2.2 mV and 2.9 mV, respectively.